TOROS: Target Oriented O(n) Recommender System

Scalability challenges in recommender systems refer to the difficulties that arise when maintaining systems that can handle growing datasets. On the other hand, state-of-the-art recommender systems are focusing only on increasing the number of transactions (by using evaluation metrics based on rating or ranking). However, the success of a recommender system may be reflected in business metrics, such as increased sales, revenue, user retention, or customer satisfaction. In this chapter, we aim to overcome these two challenges together: “how to define own targets (evaluation metrics) on a recommender system?” and in the meanwhile “how to scale it on big data?”. We proposed a collaborative filtering method called “TOROS: Target Oriented O(n) Recommender System”. TOROS reduces the similarity calculation complexity from O(n2) to O(n) and it has been evaluated on both publicly available datasets and also real-world e-commerce datasets of an e-commerce services provider company Frizbit S.L. We have compared TOROS with state-of-the-art recommender system algorithms and evaluated based on time and space consumption yet. As future work, we also evaluate the efficiency of TOROS in terms of various business-specific targets.